Under-Review

We present VMS, a unified whole-body controller that enables humanoid robots to learn diverse and dynamic behaviors within a single policy through hybrid tracking and orthogonal mixture of experts.

We propose a novel framework that enables humanoid robots to traverse complex terrains with controllable human-like gaits using a mixture of latent residual experts and multi-discriminators.

We propose the Learn as Individuals, Evolve as a Team (LIET) framework to enable multi-agent LLMs to adapt to embodied environments through individual learning and team evolution

We propose a novel bimanual foundation policy that leverages text-to-video models to predict robot trajectories and uses optical flow as an intermediate variable to improve generalization.

We propose Align-Then-stEer (ATE), a framework that adapts VLAs to novel robots and tasks through unified latent guidance. ATE can handle significant domain shifts without compromising performance and compatible to Pi0, RDT, and etc.